To better understand the impact of learning paradigms when scaling compute, we double the model parameters (up to 750,000) and train on tens times more data (12.8M samples) for AR architectures.
We monitor optimality gap on the training size range (TSP20-50) as well as a larger size (TSP100) vs. the number of training samples.
In Figure REF , we see that increasing model capacity leads to better learning.
Notably, RL models, which train on unique randomly generated samples throughout, are able to keep improving their performance within as well as outside of training size range as they see more samples.
On the other hand, SL is bottlenecked by the need for optimal groundtruth solutions: SL models iterate over the same 1.28M unique labelled samples and stop improving at a point.
Beyond favorable inductive biases, more sample-efficient RL algorithms [1]} may be a key ingredient for learning from larger TSPs beyond tens of nodes.
